Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "131" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460009 | not_connected | 100.00% | 99.95% | 99.95% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.2224 | 0.4558 | 0.4444 | nan | nan |
| 2460008 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460007 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459999 | not_connected | 0.00% | 0.08% | 17.88% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5970 | 0.2544 | 0.4191 | nan | nan |
| 2459998 | not_connected | 100.00% | 0.00% | 12.44% | 0.00% | - | - | -0.565179 | 11.467942 | -0.111486 | 4.845214 | -0.335380 | 9.039108 | -1.239049 | 0.597234 | 0.5895 | 0.2629 | 0.4229 | nan | nan |
| 2459997 | not_connected | 100.00% | 0.00% | 12.65% | 0.00% | - | - | -0.521363 | 12.783206 | 0.127076 | 5.372693 | -0.070683 | 8.572654 | -1.483594 | 0.416877 | 0.6069 | 0.2546 | 0.4248 | nan | nan |
| 2459996 | not_connected | 100.00% | 0.00% | 19.14% | 0.00% | - | - | -0.755573 | 13.664640 | -0.067393 | 6.756148 | -0.501678 | 8.048474 | -0.714870 | 0.203539 | 0.6039 | 0.2512 | 0.4415 | nan | nan |
| 2459995 | not_connected | 100.00% | 0.00% | 8.97% | 0.00% | - | - | -0.517235 | 13.683014 | -0.037793 | 5.983374 | -0.705915 | 8.370824 | -0.673212 | 0.016449 | 0.6023 | 0.2729 | 0.4247 | nan | nan |
| 2459994 | not_connected | 100.00% | 0.00% | 7.78% | 0.00% | - | - | -0.594554 | 13.311429 | -0.023634 | 5.209502 | -0.717521 | 8.432241 | -0.157448 | 0.637326 | 0.5996 | 0.2721 | 0.4237 | nan | nan |
| 2459993 | not_connected | 100.00% | 0.00% | 23.13% | 0.00% | - | - | -0.319302 | 12.680997 | 0.215652 | 4.550246 | -0.524395 | 9.735788 | -0.802870 | 1.036473 | 0.5898 | 0.2628 | 0.4235 | nan | nan |
| 2459991 | not_connected | 100.00% | 0.00% | 3.93% | 0.00% | - | - | -0.267858 | 15.296495 | 0.116917 | 4.888239 | -0.883533 | 9.295809 | -0.711395 | -0.209071 | 0.6015 | 0.2782 | 0.4319 | nan | nan |
| 2459990 | not_connected | 100.00% | 0.00% | 8.37% | 0.00% | - | - | -0.388135 | 12.663893 | 0.097411 | 4.646154 | -0.677243 | 9.647910 | -0.760599 | -0.388212 | 0.5967 | 0.2741 | 0.4279 | nan | nan |
| 2459989 | not_connected | 100.00% | 0.00% | 3.30% | 0.00% | - | - | -0.823688 | 12.871261 | 0.279451 | 4.376268 | -0.740912 | 8.044638 | -0.850241 | -0.347884 | 0.5989 | 0.2742 | 0.4300 | nan | nan |
| 2459988 | not_connected | 100.00% | 0.00% | 5.24% | 0.00% | - | - | -0.649909 | 15.123210 | 0.080236 | 4.743045 | -0.508185 | 11.614772 | -0.784138 | -0.368279 | 0.6009 | 0.2793 | 0.4228 | nan | nan |
| 2459987 | not_connected | 100.00% | 0.00% | 12.64% | 0.00% | - | - | -0.250321 | 12.833213 | -0.010642 | 4.962346 | -0.960167 | 7.011113 | -1.229156 | 0.036532 | 0.6042 | 0.2709 | 0.4182 | nan | nan |
| 2459986 | not_connected | 100.00% | 0.00% | 4.64% | 0.00% | - | - | -0.832242 | 15.563129 | 0.012548 | 5.251768 | -0.913677 | 10.149022 | -0.973336 | 5.918461 | 0.6321 | 0.3387 | 0.3851 | nan | nan |
| 2459985 | not_connected | 100.00% | 0.00% | 7.08% | 0.00% | - | - | -0.045065 | 14.213866 | -0.063235 | 4.979726 | -0.775436 | 7.570711 | -1.081897 | -0.036325 | 0.6065 | 0.2739 | 0.4283 | nan | nan |
| 2459984 | not_connected | 100.00% | 0.00% | 4.92% | 0.00% | - | - | -0.314482 | 13.582089 | 0.063718 | 5.269751 | -0.977349 | 10.211850 | -1.119237 | 1.065217 | 0.6203 | 0.2998 | 0.4092 | nan | nan |
| 2459983 | not_connected | 100.00% | 0.00% | 0.05% | 0.00% | - | - | -0.876459 | 13.210908 | 0.032062 | 4.699887 | -1.146183 | 9.925664 | -1.181454 | 3.238671 | 0.6359 | 0.3610 | 0.3704 | nan | nan |
| 2459982 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.668469 | 10.189504 | -0.471293 | 4.141703 | -0.787388 | 4.787697 | -1.148567 | 0.675589 | 0.6804 | 0.4127 | 0.3599 | nan | nan |
| 2459981 | not_connected | 100.00% | 0.00% | 3.19% | 0.00% | - | - | -0.808582 | 12.187279 | 0.129515 | 4.791359 | -1.056825 | 10.816495 | -0.895509 | -0.324946 | 0.6077 | 0.2844 | 0.4270 | nan | nan |
| 2459980 | not_connected | 100.00% | 0.00% | 0.22% | 0.00% | - | - | -0.889682 | 11.689055 | -0.180230 | 4.494288 | -1.087539 | 9.597271 | -1.154615 | 2.711740 | 0.6503 | 0.3661 | 0.3747 | nan | nan |
| 2459979 | not_connected | 100.00% | 0.00% | 6.33% | 0.00% | - | - | -0.882284 | 12.230984 | -0.119066 | 4.066393 | -0.721420 | 8.651858 | -0.821435 | -0.038513 | 0.6019 | 0.2831 | 0.4267 | nan | nan |
| 2459978 | not_connected | 100.00% | 0.00% | 9.41% | 0.00% | - | - | -0.877798 | 12.509860 | 0.027705 | 4.388703 | -0.817205 | 9.613030 | -1.253987 | -0.513860 | 0.6016 | 0.2746 | 0.4350 | nan | nan |
| 2459977 | not_connected | 100.00% | 0.00% | 5.13% | 0.00% | - | - | -0.727817 | 13.033221 | -0.095344 | 4.537389 | -0.563766 | 9.625106 | -0.790768 | 0.278989 | 0.5684 | 0.2553 | 0.3918 | nan | nan |
| 2459976 | not_connected | 100.00% | 0.00% | 8.16% | 0.00% | - | - | -0.781278 | 12.673186 | -0.109890 | 4.560069 | -0.787569 | 9.548361 | -1.141690 | 0.081699 | 0.6109 | 0.2861 | 0.4287 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 11.467942 | -0.565179 | 11.467942 | -0.111486 | 4.845214 | -0.335380 | 9.039108 | -1.239049 | 0.597234 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 12.783206 | -0.521363 | 12.783206 | 0.127076 | 5.372693 | -0.070683 | 8.572654 | -1.483594 | 0.416877 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 13.664640 | -0.755573 | 13.664640 | -0.067393 | 6.756148 | -0.501678 | 8.048474 | -0.714870 | 0.203539 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 13.683014 | -0.517235 | 13.683014 | -0.037793 | 5.983374 | -0.705915 | 8.370824 | -0.673212 | 0.016449 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 13.311429 | -0.594554 | 13.311429 | -0.023634 | 5.209502 | -0.717521 | 8.432241 | -0.157448 | 0.637326 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 12.680997 | -0.319302 | 12.680997 | 0.215652 | 4.550246 | -0.524395 | 9.735788 | -0.802870 | 1.036473 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 15.296495 | -0.267858 | 15.296495 | 0.116917 | 4.888239 | -0.883533 | 9.295809 | -0.711395 | -0.209071 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 12.663893 | 12.663893 | -0.388135 | 4.646154 | 0.097411 | 9.647910 | -0.677243 | -0.388212 | -0.760599 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 12.871261 | 12.871261 | -0.823688 | 4.376268 | 0.279451 | 8.044638 | -0.740912 | -0.347884 | -0.850241 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 15.123210 | 15.123210 | -0.649909 | 4.743045 | 0.080236 | 11.614772 | -0.508185 | -0.368279 | -0.784138 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 12.833213 | -0.250321 | 12.833213 | -0.010642 | 4.962346 | -0.960167 | 7.011113 | -1.229156 | 0.036532 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 15.563129 | 15.563129 | -0.832242 | 5.251768 | 0.012548 | 10.149022 | -0.913677 | 5.918461 | -0.973336 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 14.213866 | 14.213866 | -0.045065 | 4.979726 | -0.063235 | 7.570711 | -0.775436 | -0.036325 | -1.081897 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 13.582089 | -0.314482 | 13.582089 | 0.063718 | 5.269751 | -0.977349 | 10.211850 | -1.119237 | 1.065217 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 13.210908 | -0.876459 | 13.210908 | 0.032062 | 4.699887 | -1.146183 | 9.925664 | -1.181454 | 3.238671 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 10.189504 | -0.668469 | 10.189504 | -0.471293 | 4.141703 | -0.787388 | 4.787697 | -1.148567 | 0.675589 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 12.187279 | 12.187279 | -0.808582 | 4.791359 | 0.129515 | 10.816495 | -1.056825 | -0.324946 | -0.895509 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 11.689055 | 11.689055 | -0.889682 | 4.494288 | -0.180230 | 9.597271 | -1.087539 | 2.711740 | -1.154615 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 12.230984 | -0.882284 | 12.230984 | -0.119066 | 4.066393 | -0.721420 | 8.651858 | -0.821435 | -0.038513 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 12.509860 | 12.509860 | -0.877798 | 4.388703 | 0.027705 | 9.613030 | -0.817205 | -0.513860 | -1.253987 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 13.033221 | -0.727817 | 13.033221 | -0.095344 | 4.537389 | -0.563766 | 9.625106 | -0.790768 | 0.278989 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | N11 | not_connected | nn Shape | 12.673186 | 12.673186 | -0.781278 | 4.560069 | -0.109890 | 9.548361 | -0.787569 | 0.081699 | -1.141690 |